Spatial Patterns of Snow Distribution for Improved Earth System Modelling in the Arctic

The spatial distribution of snow plays a vital role in Arctic climate, hydrology, and ecology due to its fundamental influence on the water balance, thermal regimes, vegetation, and carbon flux. However, for earth system modelling, the spatial distribution of snow is not well understood, and therefore, it is not well modeled, which can lead to substantial uncertainties in snow cover representations. To capture key hydro-ecological controls on snow spatial distribution, we carried out intensive field studies over multiple years for two small (2017–2019, ~2.5 km2) sub-Arctic study sites located on the Seward Peninsula of Alaska. Using an intensive suite of field observations (> 22,000 data points), we developed simple models of spatial distribution of snow water equivalent (SWE) using factors such as topographic characteristics, vegetation characteristics based on greenness (normalized different vegetation index, NDVI), and a simple metric for approximating winds. The most successful model was the random forest using both study sites and all years, which was able to accurately capture the complexity and variability of snow characteristics across the sites. Approximately 86 % of the SWE distribution could be accounted for, on average, by the random forest model at the study sites. Factors that impacted year-to-year snow distribution included NDVI, elevation, and a metric to represent coarse microtopography (topographic position index, or TPI), while slope, wind, and fine microtopography factors were less important. The models were used to predict SWE at the locations through the study area and for all years. The characterization of the SWE spatial distribution patterns and the statistical relationships developed between SWE and its impacting factors will be used for the improvement of snow distribution modelling in the Department of Energy’s earth system model, and to improve understanding of hydrology, topography, and vegetation dynamics in the Arctic and sub-Arctic regions of the globe.

Global anthropogenic CO₂ emissions and uncertainties as a prior for Earth system modelling and data assimilation

The growth in anthropogenic carbon dioxide (CO2) emissions acts as a major climate change driver, which has widespread implications across society, influencing the scientific, political, and public sectors. For an increased understanding of the CO2 emission sources, patterns, and trends, a link between the emission inventories and observed CO2 concentrations is best established via Earth system modelling and data assimilation. Bringing together the different pieces of the puzzle of a very different nature (measurements, reported statistics, and models), it is of utmost importance to know their level of confidence and boundaries well. Inversions disaggregate the variation in observed atmospheric CO2 concentration to variability in CO2 emissions by constraining the regional distribution of CO2 fluxes, derived either bottom-up from statistics or top-down from observations. The level of confidence and boundaries for each of these CO2 fluxes is as important as their intensity, though often not available for bottom-up anthropogenic CO2 emissions. This study provides a postprocessing tool CHE_UNC_APP for anthropogenic CO2 emissions to help assess and manage the uncertainty in the different emitting sectors. The postprocessor is available under https://doi.org/10.5281/zenodo.5196190 (Choulga et al., 2021). Recommendations are given for regrouping the sectoral emissions, taking into account their uncertainty instead of their statistical origin; for addressing local hot spots; for the treatment of sectors with small budget but uncertainties larger than 100 %; and for the assumptions around the classification of countries based on the quality of their statistical infrastructure. This tool has been applied to the EDGARv4.3.2_FT2015 dataset, resulting in seven input grid maps with upper- and lower-half ranges of uncertainty for the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System. The dataset is documented and available under https://doi.org/10.5281/zenodo.3967439 (Choulga et al., 2020). While the uncertainty in most emission groups remains relatively small (5 %–20 %), the largest contribution (usually over 40 %) to the total uncertainty is determined by the OTHER group (of fuel exploitation and transformation but also agricultural soils and solvents) at the global scale. The uncertainties have been compared for selected countries to those reported in the inventories submitted to the United Nations Framework Convention on Climate Change and to those assessed for the European emission grid maps of the Netherlands Organisation for Applied Scientific Research. Several sensitivity experiments are performed to check (1) the country dependence (by analysing the impact of assuming either a well- or less well-developed statistical infrastructure), (2) the fuel type dependence (by adding explicit information for each fuel type used per activity from the Intergovernmental Panel on Climate Change), and (3) the spatial source distribution dependence (by aggregating all emission sources and comparing the effect against an even redistribution over the country). The first experiment shows that the SETTLEMENTS group (of energy for buildings) uncertainty changes the most when development level is changed. The second experiment shows that fuel-specific information reduces uncertainty in emissions only when a country uses several different fuels in the same amount; when a country mainly uses the most globally typical fuel for an activity, uncertainty values computed with and without detailed fuel information are the same. The third experiment highlights the importance of spatial mapping.

ESM-Tools version 5.0: a modular infrastructure for stand-alone and coupled Earth system modelling (ESM)

Earth system and climate modelling involves the simulation of processes on a wide range of scales and within and across various compartments of the Earth system. In practice, component models are often developed independently by different research groups, adapted by others to their special interests and then combined using a dedicated coupling software. This procedure not only leads to a strongly growing number of available versions of model components and coupled setups but also to model- and high-performance computing (HPC)-system-dependent ways of obtaining, configuring, building and operating them. Therefore, implementing these Earth system models (ESMs) can be challenging and extremely time consuming, especially for less experienced modellers or scientists aiming to use different ESMs as in the case of intercomparison projects. To assist researchers and modellers by reducing avoidable complexity, we developed the ESM-Tools software, which provides a standard way for downloading, configuring, compiling, running and monitoring different models on a variety of HPC systems. It should be noted that ESM-Tools is not a coupling software itself but a workflow and infrastructure management tool to provide access to increase usability of already existing components and coupled setups. As coupled ESMs are technically the more challenging tasks, we will focus on coupled setups, always implying that stand-alone models can benefit in the same way. With ESM-Tools, the user is only required to provide a short script consisting of only the experiment-specific definitions, while the software executes all the phases of a simulation in the correct order. The software, which is well documented and easy to install and use, currently supports four ocean models, three atmosphere models, two biogeochemistry models, an ice sheet model, an isostatic adjustment model, a hydrology model and a land-surface model. Compared to previous versions, ESM-Tools has lately been entirely recoded in a high-level programming language (Python) and provides researchers with an even more user-friendly interface for Earth system modelling. ESM-Tools was developed within the framework of the Advanced Earth System Model Capacity project, supported by the Helmholtz Association.

The Frontiers of Deep Learning for Earth System Modelling

<p>This talk gives an overview of cutting-edge artificial intelligence applications and techniques for the earth-system sciences. We survey the most important recent contributions in areas including extreme weather, physics emulation, nowcasting, medium-range forecasting, uncertainty quantification, bias-correction, generative adversarial networks, data in-painting, network-HPC coupling, physics-informed neural nets, and geoengineering, amongst others. Then, we describe recent AI breakthroughs that have the potential to be of greatest benefit to the geosciences. We also discuss major open challenges in AI for science and their potential solutions. This talk is a living document, in that it is updated frequently, in order to accurately relect this rapidly changing field.</p>

ESM-Tools Version 5.0: A modular infrastructure for stand-alone and coupled Earth System Modelling (ESM)

<p>During the last two decades, modern societies have gradually understood the urge to tackle the climate change challenge, and consequently, a growing number of national and international initiatives have been launched with the aim of better understanding the Earth System. In this context, Earth System Modelling (ESM) has rapidly expanded, leading to a large number of research groups targeting the many components of the system at different scales and with different levels of interactions between components. This has led to the development of increasing number of models, couplings, versions tuned to address different scales or scenarios, and model-specific compilation and operating procedures. This operational complexity makes the implementation of multiple models excessively time consuming especially for less experienced modellers.</p><p>ESM-Tools is an open-source modular software written in Python, aimed to overcome many of the difficulties associated to the operation of ESMs. ESM-Tools allows for downloading, compiling and running a wide range of ESM models and coupled setups in the most important HPC facilities available in Germany. It currently supports multiple models for ocean, atmosphere, biochemistry, ice sheet, isostatic adjustment, hydrology, and land-surface, and six ocean-atmosphere and two ice-sheet-ocean-atmosphere coupled setups, through two couplers (included modularly through ESM-Interface). The tools are coded in Python while all the component and coupling information is contained in easy-to-read YAML files. The front-end user is required to provide only a short script written in YAML format, containing the experiment specific definitions. This user-friendly interface makes ESM-Tools a convenient software for training and educational purposes. Simultaneously, its modularity and the separation between the component-specific information and tool scripts facilitates the implementation and maintenance of new components, couplings and versions. ESM-Tools team of scientific programmers provides also user support, workshops and detailed documentation. The ESM-Tools were developed within the framework of the project Advance Earth System Model Capacity, supported by Helmholtz Association and has become one of the main pillars of the German infrastructure for Climate Modelling.</p>

The SERGHEI model and its core shallow water solver

<p>The Simulation Environment for Geomorphology, Hydrodynamics and Ecohydrology in Integrated form (SERGHEI) model framework is a multi-dimensional, multi-domain and multi-physics model framework. It is designed to provide a modelling environment for hydrodynamics, ecohydrology, morphodynamics, and, importantly, interactions and feedbacks among such processes, at different levels of complexity and across spatiotemporal scales. SERGHEI is in essence, a terrestrial landscape simulator based on a hydrodynamics core, designed with an outlook towards Earth System Modelling applications. Consequently, efficient mathematical and numerical formulations, as well as HPC implementations are at its core. SERGHEI intends to enable large scale and high resolution problems, which will allow to acknowledge and simulate emergent behaviours rising from the small-scale interactions and feedbacks between different environmental processes, that often manifest at larger spatiotemporal scales.</p><p>At the core of the technical innovation in SERGHEI is its HPC implementation, built from scratch on the Kokkos programming model and C++ library. This approach facilitates portability from personal computers to Tier-0 HPC systems, including GPU-based and heterogeneous systems. This is achieved by relying on Kokkos handling memory models, thread management and computational policies for the required backend programming models. In particular, using Kokkos, SERGHEI can be compiled for multiple CPUs and GPUs using a combination of OpenMP, MPI, and CUDA.</p><p>In this contribution, we introduce the SERGHEI model framework, and specially its first operational module for solving shallow water equations (SERGHEI-SWE). This module is designed to be applicable to hydrological, environmental and consequently Earth System Modelling problems, but also to classical engineering problems such as fluvial or urban flood modelling. We also provide a first showcase of the applicability of the SERGHEI-SWE solver to several well-known benchmarks, and the performance of the solver on large-scale hydrological simulation and flooding problems. We also show and discuss the scaling properties of the solver (on several Tier-0 systems)  and sketch out its current and future development.</p>